AI Murder, Openness in AI, Spectrum Challenge, and SDR Apps

Mika Model (Slate) — a new story from Paolo Bacigalupi, asking whether an AI’d up sex doll could commit murder. The companion article by Ryan Calo takes the story’s situation seriously and tries to answer that question, and is fascinating.

Strategic Implications of Openness in AI Development (Nick Bostrom, PDF) — Some forms of openness are plausibly positive on both counts (openness about safety measures, openness about goals). Others (openness about source code, science, and possibly capability) could e.g. lead to a tightening of the competitive situation around the time of the introduction of advanced AI, increasing the probability that winning the AI race is incompatible with using any safety method that incurs a delay or limits performance. We identify several key factors that must be taken into account by any well-founded opinion on the matter. An argument for a closed-source singularity.

LimeSDR — a low cost, open source, apps-enabled (more on that later) software defined radio (SDR) platform that can be used to support just about any type of wireless communication standard. With an App Store (via Snappy Ubuntu Core’s app distribution platform).

Tuning Fanout, Moore's Law, 3D Everything, and Social Graph Analysis

Facebook’s Mystery Machine — The goal of this paper is very similar to that of Google Dapper[…]. Both work [to] try to figure out bottlenecks in performance in high fanout large-scale Internet services. Both work us[ing] similar methods, however this work (the mystery machine) tries to accomplish the task relying on less instrumentation than Google Dapper. The novelty of the mystery machine work is that it tries to infer the component call graph implicitly via mining the logs, where as Google Dapper instrumented each call in a meticulous manner and explicitly obtained the entire call graph.

The Multiple Lives of Moore’s Law — A shrinking transistor not only allowed more components to be crammed onto an integrated circuit but also made those transistors faster and less power hungry. This single factor has been responsible for much of the staying power of Moore’s Law, and it’s lasted through two very different incarnations. In the early days, a phase I call Moore’s Law 1.0, progress came by “scaling up”—adding more components to a chip. At first, the goal was simply to gobble up the discrete components of existing applications and put them in one reliable and inexpensive package. As a result, chips got bigger and more complex. The microprocessor, which emerged in the early 1970s, exemplifies this phase. But over the last few decades, progress in the semiconductor industry became dominated by Moore’s Law 2.0. This era is all about “scaling down,” driving down the size and cost of transistors even if the number of transistors per chip does not go up.

The Care and Feeding of Weird Machines Found in Executable Metadata (YouTube) — talk from 29th Chaos Communication Congress, on using tricking the ELF linker/loader into arbitrary computation from the metadata supplied. Yes, there’s a brainfuck compiler that turns code into metadata which is then, through a supernatural mix of pixies, steam engines, and binary, executed. This will make your brain leak. Weird machines are everywhere.

European Libraries May Digitise Books Without Permission — “The right of libraries to communicate, by dedicated terminals, the works they hold in their collections would risk being rendered largely meaningless, or indeed ineffective, if they did not have an ancillary right to digitize the works in question,” the court said. Even if the rights holder offers a library the possibility of licensing his works on appropriate terms, the library can use the exception to publish works on electronic terminals, the court ruled. “Otherwise, the library could not realize its core mission or promote the public interest in promoting research and private study,” it said.

Laws of Crappy Dashboards — (caution, NSFW language … “crappy” is my paraphrase) so true. Not talking to users will result in a [crappy] dashboard. You don’t know if the dashboard is going to be useful. But you don’t talk to the users to figure it out. Or you just show it to them for a minute (with someone else’s data), never giving them a chance to figure out what the hell they could do with it if you gave it to them.

On Being a Senior Engineer (Etsy) — Mature engineers know that no matter how complete, elegant, or superior their designs are, it won’t matter if no one wants to work alongside them because they are assholes.

Control Theory (Coursera) — Learn about how to make mobile robots move in effective, safe, predictable, and collaborative ways using modern control theory. (via DIY Drones)

US Moves Towards Open Access (WaPo) — Congress passed a budget that will make about half of taxpayer-funded research available to the public.

NHS Patient Data Available for Companies to Buy (The Guardian) — Once live, organisations such as university research departments – but also insurers and drug companies – will be able to apply to the new Health and Social Care Information Centre (HSCIC) to gain access to the database, called care.data. If an application is approved then firms will have to pay to extract this information, which will be scrubbed of some personal identifiers but not enough to make the information completely anonymous – a process known as “pseudonymisation”. Recipe for disaster as it has been repeatedly shown that it’s easy to identify individuals, given enough scrubbed data. Can’t see why the NHS just doesn’t make it an app in Facebook. “Nat’s Prostate status: it’s complicated.”